import torch
import torchvision
from torch import nn as nn
from d2l import torch as d2l
from torch.nn import functional as F

# FCN:
#     采用卷积神经网络实现了从图像像素到像素类别的变换
# 在全卷积网络中，我们可以将转置卷积层初始化为双线性插值的上采样
#
#


def bilinear_kernel(in_channels, out_channels, kernel_size):
    """双线性插值上采样"""
    factor = (kernel_size + 1) // 2
    if kernel_size % 2 == 1:
        center = factor - 1
    else:
        center = factor - 0.5
    og = (torch.arange(kernel_size).reshape(-1, 1),
          torch.arange(kernel_size).reshape(1, -1))
    filt = (1 - torch.abs(og[0] - center) / factor) * \
           (1 - torch.abs(og[1] - center) / factor)
    weight = torch.zeros((in_channels, out_channels,
                          kernel_size, kernel_size))
    weight[range(in_channels), range(out_channels), :, :] = filt
    return weight


# 网络的定义
# net
pretrained_net = torchvision.models.resnet18(pretrained=True)
net = nn.Sequential(*list(pretrained_net.children())[:-2])

# 输出层变幻
num_classes = 21
net.add_module('final_conv', nn.Conv2d(512, num_classes, kernel_size=1))
net.add_module('transpose_conv', nn.ConvTranspose2d(num_classes, num_classes, kernel_size=64, padding=16, stride=32))

# 转置卷积放大
conv_trans = nn.ConvTranspose2d(3, 3, kernel_size=4, padding=1, stride=2, bias=False)
# 初始化
conv_trans.weight.data.copy_(bilinear_kernel(3, 3, 4))

# Xavier初始化参数
W = bilinear_kernel(num_classes, num_classes, 64)
net.transpose_conv.weight.data.copy_(W)


# load data
batch_size, crop_size = 32, (320, 480)
train_iter, test_iter = d2l.load_data_voc(batch_size, crop_size)


# loss
def loss(inputs, targets):
    return F.cross_entropy(inputs, targets, reduction='none').mean(1).mean(1)


def predict(img):
    X = test_iter.dataset.normalize_image(img).unsqueeze(0)
    pred = net(X.to(devices[0])).argmax(dim=1)
    return pred.reshape(pred.shape[1], pred.shape[2])


def label2image(pred):
    colormap = torch.tensor(d2l.VOC_COLORMAP, device=devices[0])
    X = pred.long()
    return colormap[X, :]


if __name__ == "__main__":
    print("模型训练：")
    num_epochs, lr, wd, devices = 5, 0.001, 1e-3, ['cpu']
    trainer = torch.optim.SGD(net.parameters(), lr=lr, weight_decay=wd)
    d2l.train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs, devices)

    print("\n模型预测：")
    voc_dir = d2l.download_extract('voc2012', 'VOCdevkit/VOC2012')
    test_images, test_labels = d2l.read_voc_images(voc_dir, False)
    n, imgs = 4, []
    for i in range(n):
        crop_rect = (0, 0, 320, 480)
    X = torchvision.transforms.functional.crop(test_images[i], *crop_rect)
    pred = label2image(predict(X))
    imgs += [X.permute(1,2,0), pred.cpu(),
             torchvision.transforms.functional.crop(
                 test_labels[i], *crop_rect).permute(1, 2, 0)]
    d2l.show_images(imgs[::3] + imgs[1::3] + imgs[2::3], 3, n, scale=2)

